Primary care practice a la carte among GPs: using organizational diversity to increase job satisfaction
Bibliographic record
Abstract
BACKGROUND: Primary care revival in Canada and elsewhere is viewed by many as conditional to the introduction of new organizational models. Endorsement by GPs is a key factor in the success of these models, and increasing GPs' job satisfaction is often one of the desired outcomes of the reforms currently underway. OBJECTIVES: The phenomenon of work satisfaction from the GP's perspective is not yet fully understood. The objectives of this study were to elicit its different facets and to understand better how organizational factors affect it. METHODS: This is a case study carried out in the province of Quebec (Canada). We conducted semi-structured interviews with 28 GPs working in private clinics and community health centres (Centre local de services communautaires). RESULTS: The main themes uncovered are related to the relationship between time management and quality of care, variation in work, autonomy in day-to-day practice, team 'orientedness' and social rewards. We also found that some GPs prefer to combine work in different organizations and models in order to increase their job satisfaction and to better cope with an increasingly complex task environment. CONCLUSION: Our study provides a comprehensive view of the various dimensions that GPs consider important in their professional life. Our findings suggest that, for many GPs, the perfect practice is tailor made and implies a combination of organizational models in order to fulfil their multiple professional goals. This has important implications for decision makers who are promoting new primary care models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".